In a classical machine learning setting, this is easily achievable by the means of a linear layer with softmax activation. In the QML framework, the calculation is carried by first putting the input qubits into the initial state (e.g. a string such as 010010), then they are entangled between each other, rotated by arbitrary angles, and finally observed (“measured”). The goal of the training is to find the rotation angles mentioned above that optimize some cost function such as the mean squared error (MSE) between the output and some pre-determined labels. This kind of quantum circuit is known as Variational Quantum Circuit (VQC) [1]}, [2]}. See Fig. REF  for VQC architecture visualization. However, a VQC cannot change the dimensionality of the input but only rotate the state of the qubits. Hence, the VQC is “dressed”, i.e. it is sandwiched between two classical linear layers to match the dimensionality. A classical layer “squeezes” the input to match the number of qubits, and another classical layer “bloats” the output to match the dimension of the hidden vectors.
